# Data frame representing information about a collection of emails These data represent incoming emails for the first three months of 2012 for an email account (see Source). data ## Format A `email` (`email_sent`) data frame has 3921 (1252) observations on the following 21 variables. - **spam**: Indicator for whether the email was spam. - **to_multiple**: Indicator for whether the email was addressed to more than one recipient. - **from**: Whether the message was listed as from anyone (this is usually set by default for regular outgoing email). - **cc**: Number of people cc'ed. - **sent_email**: Indicator for whether the sender had been sent an email in the last 30 days. - **time**: Time at which email was sent. - **image**: The number of images attached. - **attach**: The number of attached files. - **dollar**: The number of times a dollar sign or the word dollar appeared in the email. - **winner**: Indicates whether winner appeared in the email. - **inherit**: The number of times inherit (or an extension, such as inheritance ) appeared in the email. - **viagra**: The number of times viagra appeared in the email. - **password**: The number of times password appeared in the email. - **num_char**: The number of characters in the email, in thousands. - **line_breaks**: The number of line breaks in the email (does not count text wrapping). - **format**: Indicates whether the email was written using HTML (e.g. may have included bolding or active links). - **re_subj**: Whether the subject started with Re: , RE: , re: , or rE: - **exclaim_subj**: Whether there was an exclamation point in the subject. - **urgent_subj**: Whether the word urgent was in the email subject. - **exclaim_mess**: The number of exclamation points in the email message. - **number**: Factor variable saying whether there was no number, a small number (under 1 million), or a big number. ## Source David Diez's Gmail Account, early months of 2012. All personally identifiable information has been removed. ```r email ``` ## Examples ```r e <- email # ______ Variables For Logistic Regression ______# # Variables are modified to match # OpenIntro Statistics, Second Edition # As Is (7): spam, to_multiple, winner, format, # re_subj, exclaim_subj # Omitted (6): from, sent_email, time, image, # viagra, urgent_subj, number # Become Indicators (5): cc, attach, dollar, # inherit, password e$cc <- ifelse(email$cc > 0, 1, 0) e$attach <- ifelse(email$attach > 0, 1, 0) e$dollar <- ifelse(email$dollar > 0, 1, 0) e$inherit <- ifelse(email$inherit > 0, 1, 0) e$password <- ifelse(email$password > 0, 1, 0) # Transform (3): num_char, line_breaks, exclaim_mess # e$num_char <- cut(email$num_char, c(0,1,5,10,20,1000)) # e$line_breaks <- cut(email$line_breaks, c(0,10,100,500,10000)) # e$exclaim_mess <- cut(email$exclaim_mess, c(-1,0,1,5,10000)) g <- glm( spam ~ to_multiple + winner + format + re_subj + exclaim_subj + cc + attach + dollar + inherit + password, # + # num_char + line_breaks + exclaim_mess, data = e, family = binomial ) summary(g) # ______ Variable Selection Via AIC ______# g. <- step(g) plot(predict(g., type = "response"), e$spam) # ______ Splitting num_char by html ______# x <- log(email$num_char) bw <- 0.004 R <- range(x) + c(-1, 1) wt <- sum(email$format == 1) / nrow(email) htmlAll <- density(x, bw = 0.4, from = R[1], to = R[2]) htmlNo <- density(x[email$format != 1], bw = 0.4, from = R[1], to = R[2] ) htmlYes <- density(x[email$format == 1], bw = 0.4, from = R[1], to = R[2] ) htmlNo$y <- htmlNo$y #* (1-wt) htmlYes$y <- htmlYes$y #* wt + htmlNo$y plot(htmlAll, xlim = c(-4, 6), ylim = c(0, 0.4)) lines(htmlNo, col = 4) lines(htmlYes, lwd = 2, col = 2) ``` ## See Also `email50`

email dataset